Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.

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Bibliographic Details
Title: Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.
Authors: Sadeghi A; Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA., Hajati F; School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, Australia., Argha A; School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW, Australia., H Lovell N; School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW, Australia., Yang M; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China., Alinejad-Rokny H; UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia. h.alinejad@unsw.edu.au.
Source: Nature communications [Nat Commun] 2026 Jun 16; Vol. 17 (1). Date of Electronic Publication: 2026 Jun 16.
Publication Type: Journal Article; Review
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2041-1723
DOI:10.1038/s41467-026-74126-5